"""
A quick start guide to graphs
==============================
"""

# sphinx_gallery_thumbnail_number = 4
# %%
#
# In this example, we show how to create graphs. We show how to create and configure its axes and its colors. We show how to create a plot based on the combination of several plots.

# %%
# The `draw` method the `Graph` class
# -----------------------------------
#
# The simplest way to create a graph is to use the `draw` method. The :class:`~openturns.Normal` distribution for example provides a method to draw its density function.

# %%
import openturns as ot
import openturns.viewer as viewer
import matplotlib.pyplot as plt


# %%
n = ot.Normal()
n

# %%
graph = n.drawPDF()
view = viewer.View(graph)

# %%
# To configure the look of the plot, we can first observe the type of graph returned by the `drawPDF` method returns: it is a :class:`~openturns.Graph`.

# %%
graph = n.drawPDF()
type(graph)

# %%
# The class:`~openturns.Graph` class provides several methods to configure the legends, the title and the colors.
# Since a graph  can contain several sub-graphs, the `setColors` method takes a list of colors as inputs argument: each item of the list corresponds to the sub-graphs.

# %%
graph.setXTitle("N")
graph.setYTitle("PDF")
graph.setTitle("Probability density function of the standard Gaussian distribution")
graph.setLegends(["N"])
graph.setColors(["blue"])
view = viewer.View(graph)

# %%
# Combine several graphs
# ----------------------
#
# In order to combine several graphs, we can use the `add` method.

# %%
# Let us create an empirical histogram from a sample.

# %%
sample = n.getSample(100)

# %%
histo = ot.HistogramFactory().build(sample).drawPDF()
view = viewer.View(histo)

# %%
# Then we add the histogram to the `graph` with the `add` method. The `graph` then contains two plots.

# %%
graph.add(histo)
view = viewer.View(graph)

# %%
# Draw a cloud
# ------------
#
# The :class:`~openturns.Cloud` class creates clouds of bidimensional points. To illustrate it, let us create two Normal distributions in two dimensions.

# %%
# Create a Funky distribution
corr = ot.CorrelationMatrix(2)
corr[0, 1] = 0.2
copula = ot.NormalCopula(corr)
x1 = ot.Normal(-1.0, 1)
x2 = ot.Normal(2, 1)
x_funk = ot.JointDistribution([x1, x2], copula)

# %%
# Create a Punk distribution
x1 = ot.Normal(1.0, 1)
x2 = ot.Normal(-2, 1)
x_punk = ot.JointDistribution([x1, x2], copula)

# %%
# Let us mix these two distributions.

# %%
mixture = ot.Mixture([x_funk, x_punk], [0.5, 1.0])

# %%
n = 500
sample = mixture.getSample(n)

# %%
graph = ot.Graph("n=%d" % (n), "X1", "X2", True, "")
cloud = ot.Cloud(sample)
graph.add(cloud)
view = viewer.View(graph)

# %%
# We sometimes want to customize the graph by choosing the type of point (square, triangle, circle, etc.), of line (continuous, dashed, etc.) or another parameter.
# We can know the list of possible values with the corresponding `getValid` method.
#
# For example, the following function returns the possible values of the `PointStyle` parameter.

# %%
ot.Drawable.GetValidPointStyles()

# %%
# The following method returns the list of colors.

# %%
ot.Drawable.GetValidColors()[0:10]

# %%
# In the following graph, we use the "aquamarine1" color with "fcircle" circles.

# %%
graph = ot.Graph("n=%d" % (n), "X1", "X2", True, "")
cloud = ot.Cloud(sample)
cloud.setColor("aquamarine1")
cloud.setPointStyle("fcircle")
graph.add(cloud)
view = viewer.View(graph)

# %%
# Configure the style of points and the thickness of a curve
# ----------------------------------------------------------
#
# Assume that we want to plot the `sine` curve from -2 to 2. The simplest way is to use the `draw` method of the function.

# %%
g = ot.SymbolicFunction("x", "sin(x)")

# %%
graph = g.draw(-2, 2)
view = viewer.View(graph)

# %%
# One would rather get a dashed curve: let us search for the available line styles.

# %%
ot.Drawable.GetValidLineStyles()


# %%
# In order to use the :class:`~openturns.Curve` class, it will be easier if we have a method to generate a :class:`~openturns.Sample` containing points regularly spaced in an interval.


# %%
def linearSample(xmin, xmax, npoints):
    """Returns a sample created from a regular grid
    from xmin to xmax with npoints points."""
    step = (xmax - xmin) / (npoints - 1)
    rg = ot.RegularGrid(xmin, step, npoints)
    vertices = rg.getVertices()
    return vertices


# %%
x = linearSample(-2, 2, 50)
y = g(x)

# %%
graph = ot.Graph("Sinus", "x", "sin(x)", True)
curve = ot.Curve(x, y)
curve.setLineStyle("dashed")
curve.setLineWidth(4)
graph.add(curve)
view = viewer.View(graph)


# %%
# Create colored curves
# ---------------------
#
# In some situations, we want to create curves with different colors.
# In this case, the following function generates a color corresponding to the `indexCurve` integer in a ensemble of `maximumNumberOfCurves` curves.


# %%
def createHSVColor(indexCurve, maximumNumberOfCurves):
    """Create a HSV color for the indexCurve-th curve
    from a sample with maximum size equal to maximumNumberOfCurves"""
    color = ot.Drawable.ConvertFromHSV(
        indexCurve * 360.0 / maximumNumberOfCurves, 1.0, 1.0
    )
    return color


# %%
pofa = ot.HermiteFactory()

# %%
graph = ot.Graph("Orthonormal Hermite polynomials", "x", "y", True, "lower right")
degreemax = 5
for k in range(degreemax):
    pk = pofa.build(k)
    curve = pk.draw(-3.0, 3.0, 50)
    curve.setLegends(["P%d" % (k)])
    curve.setColors([createHSVColor(k, degreemax)])
    graph.add(curve)
view = viewer.View(graph)

# %%
# Create matrices of graphs
# -------------------------
#
# The library provides features to create a grid of graphs. However, we can use the `add_subplot` function from `Matplotlib`.
#
# Let us create two graphs of the PDF and CDF of the following Normal distribution..

# %%
n = ot.Normal()
myPDF = n.drawPDF()
myCDF = n.drawCDF()

# %%
# Using `~openturns.GridLayout`.
grid = ot.GridLayout(1, 2)
grid.setGraph(0, 0, myPDF)
grid.setGraph(0, 1, myCDF)
_ = viewer.View(grid)

# %%
# Another method is to create a figure with the `figure` function from `Matplotlib`,
# then add two graphs with the `add_subplot` function.
# We use the `viewer.View` function to create the required `Matplotlib` object.
# Since we are not interested by the output of the `View` function, we use the dummy variable `_` as output.
# The title is finally configured with `suptitle`.

# %%
fig = plt.figure(figsize=(12, 4))
ax_pdf = fig.add_subplot(1, 2, 1)
_ = viewer.View(myPDF, figure=fig, axes=[ax_pdf])
ax_cdf = fig.add_subplot(1, 2, 2)
_ = viewer.View(myCDF, figure=fig, axes=[ax_cdf])
_ = fig.suptitle("The gaussian")

# %%
# Save a plot into a file
# -----------------------

# %%
# The :class:`openturns.viewer.View` class has a `save` method which saves the graph into an image.

# %%

# %%
n = ot.Normal()
graph = n.drawPDF()
view = viewer.View(graph)
view.save("normal.png")

# %%
# We can use the `dpi` option to configure the resolution in dots per inch.

# %%
view.save("normal-100dpi.png", dpi=100)

# %%
# Configure the size of a graph with `matplotlib`
# -----------------------------------------------

# %%

# %%
# We first create a graph containing the PDF of a Normal distribution

# %%
n = ot.Normal()
graph = n.drawPDF()

# %%
# The `figure_kw` keyword argument sets the optional arguments of the figure. In the following statement, we set the figure size in inches

# %%
view = viewer.View(graph, figure_kw={"figsize": (12, 8)})

# %%
# The `getFigure` method returns the current figure. This allows one to configure it as any other Matplotlib figure. In the following example, we configure the `suptitle`.

# %%
fig = view.getFigure()
fig.suptitle("The suptitle")
fig

# %%
# The `plot_kw` optional argument sets the arguments of the plot. In the following example, we set the color of the plot in blue.

# %%
view = viewer.View(graph, plot_kw={"color": "blue"})
plt.show()
